ENCePP Guide on Methodological Standards in Pharmacoepidemiology

5.3.7. Handling time-dependent confounding in the analysis

Methods for
dealing with time-dependent confounding (Stat Med. 2013;32(9):1584-618)
provides an overview of how time-dependent confounding can be handled in the
analysis of a study. It provides an in-depth discussion of marginal structural
models and g-computation.

MSMs have two major advantages
over G-estimation. Even if it is useful for survival time outcomes, continuous
measured outcomes and Poisson count outcomes, logistic G-estimation cannot be
conveniently used to estimate the effect of treatment on dichotomous outcomes
unless the outcome is rare. The second major advantage of MSMs is that they
resemble standard models, whereas G-estimation does not (see Marginal Structural Models to Estimate the Causal Effect of
Zidovudine on the Survival of HIV-Positive Men. Epidemiology
2000;11:561–70).

Beyond the approaches proposed
above, traditional and efficient approaches to deal with time dependent
variables should be considered in the design of the study, such as nested case
control studies with assessment of time varying exposure windows.